Content-based image retrieval
05 Nov 2008-pp 1-6
TL;DR: The presented paper gives some ideas that should clarify the raised questions and investigates the application of computer vision to content-based image retrieval of digital images in large databases.
Abstract: A picture is worth a thousand words. Yes, but which ones? Content-based image retrieval (CBIR) is the application of computer vision to the image retrieval problem. The image retrieval problem is the problem of searching for digital images in large databases. ldquoContent-basedrdquo means that the search will analyze the actual content of the image. The term dasiacontentpsila in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, search must rely on metadata such as captions or keywords, which may be laborious or expensive to produce. How this problem is used in real world examples? Can algorithms really guess the similarity which is subjective category? Can similarity works without the context? How this reflects the consumer approach to online services? The presented paper gives some ideas that should clarify the raised questions.
Citations
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26 Feb 2010
TL;DR: This paper outlines a description of the primitive feature extraction techniques like: texture, color, and shape and once these features are extracted and used as the basis for a similarity check between images the various matching techniques are discussed.
Abstract: The purpose of this Paper is to describe our research on Feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this paper outlines a description of the primitive feature extraction techniques like: texture, color, and shape. Once these features are extracted and used as the basis for a similarity check between images the various matching techniques are discussed. Furthermore, the results of its performance are illustrated by a detailed example.
30 citations
01 Jan 2010
TL;DR: In this article, the authors describe different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system and the results of their performance are illustrated by a detailed example.
Abstract: The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this paper outlines a description of the primitive feature extraction techniques like: texture, colour, and shape. Once these features are extracted and used as the basis for a similarity check between images, the various matching techniques are discussed. Furthermore, the results of its performance are illustrated by a detailed example.
16 citations
01 Jan 2012
TL;DR: Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA), which effectively see only the global Euclidean structure, GIRA is designed for discovering the local manifold structure.
Abstract: This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. These features are stored in vectors called feature vectors and compared to the feature vectors of query image and thus, the image database is sorted in decreasing order of similarity. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA), which effectively see only the global Euclidean structure, GIRA is designed for discovering the local manifold structure. Therefore, GIRA is likely to be more suitable for image retrieval, where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image; thus, the retrieval performance can be enhanced.
10 citations
Patent•
27 Feb 2014
TL;DR: In this paper, the memory stores instructions that when executed by the processor cause the processor to identify a keyword from machine-readable text, identify a contact center resource based on the identified keyword, and invoke an action based on analysis of the keyword associated with the contact centre resource; monitor the action and report results in response.
Abstract: An apparatus includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to: identify a keyword from machine-readable text; identify a contact center resource based on the identified keyword; update a first group of keywords associated with the contact center resource based on the identified keyword; invoke an action based on analysis of the keyword associated with the contact center resource; monitor the action and report results in response; and update a second group of keywords according to analysis of the results.
9 citations
Posted Content•
TL;DR: In this article, a CBIR system which utilizes visual contents (color, texture, shape and shape) of an image to retrieve images, is proposed, which builds three feature vectors and stores them into MySQL database.
Abstract: Content-Based Image Retrieval (CBIR) systems have been widely used for a wide range of applications such as Art collections, Crime prevention and Intellectual property. In this paper, a novel CBIR system, which utilizes visual contents (color, texture and shape) of an image to retrieve images, is proposed. The proposed system builds three feature vectors and stores them into MySQL database. The first feature vector uses descriptive statistics to describe the distribution of data in each channel of RGB channels of the image. The second feature vector describes the texture using eigenvalues of the 39 sub-bands that are generated after applying four levels 2D DWT in each channel (red, green and blue channels) of the image. These wavelets sub-bands perfectly describes the horizontal, vertical and diagonal edges that exist in the multi-resolution analysis of the image. The third feature vector describes the basic shapes that exist in the skeletonization version of the black and white representation of the image. Experimental results on a private MYSQL database that consists of 10000 images, using color, texture, shape and stored relevance feedbacks, showed 96.4% average correct retrieval rate in an efficient recovery time.
1 citations
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TL;DR: For instance, in the case of an individual in the presence of others, it can be seen as a form of involuntary expressive behavior as discussed by the authors, where the individual will have to act so that he intentionally or unintentionally expresses himself, and the others will in turn have to be impressed in some way by him.
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"Content-based image retrieval" refers background in this paper
...Currently there are two general approaches to the storage and retrieval problems of digital images....
[...]
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TL;DR: A framework for webbed customer innovation tools is offered by introducing the concept of the customer-integration cube (CIC), which renders a systematisation of webbedCustomer innovation tools on the basis of specific dimensions, which were identified as most important, and serves as an originator to reveal possible lacks of webBed customer innovation attempts.
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58 citations